Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:12, 825KB/s]                             
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:10<00:00, 5.64KFile/s]
Downloading celeba: 1.44GB [00:19, 74.9MB/s]                            
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f8b09fd84e0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f8b09f05470>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    input_real = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, shape=(None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
    
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False, is_train=True, alpha=0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        
        # input layer is 28 x 28 x 3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        # 14 x 14 x 64
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=is_train)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        # 7x7x128
        x3 = tf.layers.conv2d(relu2, 256, 4, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=is_train)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """

    with tf.variable_scope('generator', reuse=not is_train):
        x1 = tf.layers.dense(z, 2 * 2 * 512 )
        x1 = tf.reshape(x1, (-1, 2, 2, 512))
        bn1 = tf.layers.batch_normalization(x1, training=is_train)
        relu1 = tf.maximum(alpha* bn1, bn1)
        
        #Input is 2* 2 * 512 ; Output is 7 * 7 * 256 
        x2 = tf.layers.conv2d_transpose(relu1, 256, 5, strides=2, padding='valid')
        bn2 = tf.layers.batch_normalization(x2, training=is_train)
        relu2 = tf.maximum(alpha * bn2, bn2)
        

        #Input is 7 * 7 * 256 / Output is 14 * 14 * 128        
        x3 = tf.layers.conv2d_transpose(relu2, 128, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=is_train)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        #Input is 14 * 14 * 128 / Output is 28 * 28 * out_channel_dim
        logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 5, strides=2, padding='same')
        out = tf.tanh(logits)
        return logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_logits = generator(input_z, out_channel_dim)
    d_real_model, d_real_logits = discriminator(input_real)
    d_fake_model, d_fake_logits = discriminator(g_logits, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real_logits, labels=tf.ones_like(d_real_model) * 0.9))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits, labels=tf.zeros_like(d_fake_model)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits, labels=tf.ones_like(d_fake_model)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss_real+d_loss_fake, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, lr, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    steps = 0
    input_real, input_z, learning_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                batch_images *= 2

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learning_rate: lr})
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learning_rate: lr})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, learning_rate: lr})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, learning_rate: lr})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, learning_rate: lr})

                if steps % 100 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{} Step {}...".format(epoch_i+1, epoch_count, steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 32
z_dim = 128
learning_rate = 0.001
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2 Step 100... Discriminator Loss: 3.0859... Generator Loss: 0.0761
Epoch 1/2 Step 200... Discriminator Loss: 0.4288... Generator Loss: 2.4632
Epoch 1/2 Step 300... Discriminator Loss: 1.6336... Generator Loss: 0.3414
Epoch 1/2 Step 400... Discriminator Loss: 1.5298... Generator Loss: 0.3910
Epoch 1/2 Step 500... Discriminator Loss: 0.4054... Generator Loss: 2.7493
Epoch 1/2 Step 600... Discriminator Loss: 1.3925... Generator Loss: 0.4598
Epoch 1/2 Step 700... Discriminator Loss: 0.3578... Generator Loss: 4.6037
Epoch 1/2 Step 800... Discriminator Loss: 1.0506... Generator Loss: 0.6971
Epoch 1/2 Step 900... Discriminator Loss: 0.9529... Generator Loss: 0.8268
Epoch 1/2 Step 1000... Discriminator Loss: 0.6966... Generator Loss: 1.2391
Epoch 1/2 Step 1100... Discriminator Loss: 2.0309... Generator Loss: 0.2422
Epoch 1/2 Step 1200... Discriminator Loss: 0.6655... Generator Loss: 1.3644
Epoch 1/2 Step 1300... Discriminator Loss: 0.3393... Generator Loss: 6.3369
Epoch 1/2 Step 1400... Discriminator Loss: 0.5705... Generator Loss: 1.7239
Epoch 1/2 Step 1500... Discriminator Loss: 1.4436... Generator Loss: 0.4625
Epoch 1/2 Step 1600... Discriminator Loss: 0.4962... Generator Loss: 2.1426
Epoch 1/2 Step 1700... Discriminator Loss: 0.3926... Generator Loss: 3.4715
Epoch 1/2 Step 1800... Discriminator Loss: 2.3704... Generator Loss: 0.2293
Epoch 2/2 Step 1900... Discriminator Loss: 0.3665... Generator Loss: 3.5659
Epoch 2/2 Step 2000... Discriminator Loss: 0.4700... Generator Loss: 2.2982
Epoch 2/2 Step 2100... Discriminator Loss: 0.7136... Generator Loss: 1.3379
Epoch 2/2 Step 2200... Discriminator Loss: 2.8046... Generator Loss: 0.1069
Epoch 2/2 Step 2300... Discriminator Loss: 1.2812... Generator Loss: 0.5473
Epoch 2/2 Step 2400... Discriminator Loss: 0.5029... Generator Loss: 2.9808
Epoch 2/2 Step 2500... Discriminator Loss: 0.3789... Generator Loss: 3.0610
Epoch 2/2 Step 2600... Discriminator Loss: 0.3488... Generator Loss: 4.1413
Epoch 2/2 Step 2700... Discriminator Loss: 0.9344... Generator Loss: 1.0152
Epoch 2/2 Step 2800... Discriminator Loss: 0.3514... Generator Loss: 4.0132
Epoch 2/2 Step 2900... Discriminator Loss: 0.4255... Generator Loss: 2.6244
Epoch 2/2 Step 3000... Discriminator Loss: 0.9907... Generator Loss: 0.8086
Epoch 2/2 Step 3100... Discriminator Loss: 0.7956... Generator Loss: 1.0504
Epoch 2/2 Step 3200... Discriminator Loss: 0.3757... Generator Loss: 3.1557
Epoch 2/2 Step 3300... Discriminator Loss: 0.4264... Generator Loss: 2.5120
Epoch 2/2 Step 3400... Discriminator Loss: 0.6105... Generator Loss: 1.4877
Epoch 2/2 Step 3500... Discriminator Loss: 0.8393... Generator Loss: 1.0783
Epoch 2/2 Step 3600... Discriminator Loss: 0.3349... Generator Loss: 5.2226
Epoch 2/2 Step 3700... Discriminator Loss: 0.4353... Generator Loss: 2.5335

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [14]:
real_size = (28,28,3)
batch_size = 32
z_size = 128
learning_rate = 0.001
beta1 = 0.4

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1 Step 100... Discriminator Loss: 1.8397... Generator Loss: 0.3944
Epoch 1/1 Step 200... Discriminator Loss: 1.5883... Generator Loss: 0.5498
Epoch 1/1 Step 300... Discriminator Loss: 1.5689... Generator Loss: 0.5388
Epoch 1/1 Step 400... Discriminator Loss: 1.8119... Generator Loss: 0.4478
Epoch 1/1 Step 500... Discriminator Loss: 1.4797... Generator Loss: 0.6297
Epoch 1/1 Step 600... Discriminator Loss: 1.9952... Generator Loss: 0.3901
Epoch 1/1 Step 700... Discriminator Loss: 1.7692... Generator Loss: 0.4015
Epoch 1/1 Step 800... Discriminator Loss: 1.6159... Generator Loss: 0.6219
Epoch 1/1 Step 900... Discriminator Loss: 1.5522... Generator Loss: 0.5744
Epoch 1/1 Step 1000... Discriminator Loss: 1.6087... Generator Loss: 0.4855
Epoch 1/1 Step 1100... Discriminator Loss: 1.5553... Generator Loss: 0.5481
Epoch 1/1 Step 1200... Discriminator Loss: 1.9158... Generator Loss: 0.3490
Epoch 1/1 Step 1300... Discriminator Loss: 1.4943... Generator Loss: 0.5882
Epoch 1/1 Step 1400... Discriminator Loss: 1.7293... Generator Loss: 0.4553
Epoch 1/1 Step 1500... Discriminator Loss: 1.6824... Generator Loss: 0.3821
Epoch 1/1 Step 1600... Discriminator Loss: 1.8804... Generator Loss: 0.3190
Epoch 1/1 Step 1700... Discriminator Loss: 1.7073... Generator Loss: 0.4301
Epoch 1/1 Step 1800... Discriminator Loss: 1.7155... Generator Loss: 0.3801
Epoch 1/1 Step 1900... Discriminator Loss: 2.0440... Generator Loss: 0.2382
Epoch 1/1 Step 2000... Discriminator Loss: 1.7998... Generator Loss: 0.3468
Epoch 1/1 Step 2100... Discriminator Loss: 1.8901... Generator Loss: 0.3001
Epoch 1/1 Step 2200... Discriminator Loss: 1.8670... Generator Loss: 0.3039
Epoch 1/1 Step 2300... Discriminator Loss: 2.1991... Generator Loss: 0.1977
Epoch 1/1 Step 2400... Discriminator Loss: 2.0844... Generator Loss: 0.2321
Epoch 1/1 Step 2500... Discriminator Loss: 2.2559... Generator Loss: 0.1772
Epoch 1/1 Step 2600... Discriminator Loss: 2.4188... Generator Loss: 0.1591
Epoch 1/1 Step 2700... Discriminator Loss: 1.9835... Generator Loss: 0.2700
Epoch 1/1 Step 2800... Discriminator Loss: 2.2686... Generator Loss: 0.1788
Epoch 1/1 Step 2900... Discriminator Loss: 2.4514... Generator Loss: 0.1466
Epoch 1/1 Step 3000... Discriminator Loss: 2.6889... Generator Loss: 0.1091
Epoch 1/1 Step 3100... Discriminator Loss: 2.4039... Generator Loss: 0.1568
Epoch 1/1 Step 3200... Discriminator Loss: 2.3693... Generator Loss: 0.1518
Epoch 1/1 Step 3300... Discriminator Loss: 2.4247... Generator Loss: 0.1516
Epoch 1/1 Step 3400... Discriminator Loss: 2.4050... Generator Loss: 0.1586
Epoch 1/1 Step 3500... Discriminator Loss: 2.7977... Generator Loss: 0.0963
Epoch 1/1 Step 3600... Discriminator Loss: 2.4207... Generator Loss: 0.1552
Epoch 1/1 Step 3700... Discriminator Loss: 2.6352... Generator Loss: 0.1218
Epoch 1/1 Step 3800... Discriminator Loss: 2.2851... Generator Loss: 0.1847
Epoch 1/1 Step 3900... Discriminator Loss: 2.5660... Generator Loss: 0.1286
Epoch 1/1 Step 4000... Discriminator Loss: 2.3641... Generator Loss: 0.1502
Epoch 1/1 Step 4100... Discriminator Loss: 2.7121... Generator Loss: 0.1108
Epoch 1/1 Step 4200... Discriminator Loss: 2.7213... Generator Loss: 0.1170
Epoch 1/1 Step 4300... Discriminator Loss: 2.0991... Generator Loss: 0.2168
Epoch 1/1 Step 4400... Discriminator Loss: 2.3998... Generator Loss: 0.1583
Epoch 1/1 Step 4500... Discriminator Loss: 2.3156... Generator Loss: 0.1794
Epoch 1/1 Step 4600... Discriminator Loss: 2.4217... Generator Loss: 0.1731
Epoch 1/1 Step 4700... Discriminator Loss: 2.2305... Generator Loss: 0.1965
Epoch 1/1 Step 4800... Discriminator Loss: 2.8878... Generator Loss: 0.1595
Epoch 1/1 Step 4900... Discriminator Loss: 2.2792... Generator Loss: 0.2124
Epoch 1/1 Step 5000... Discriminator Loss: 2.4718... Generator Loss: 0.1476
Epoch 1/1 Step 5100... Discriminator Loss: 2.8128... Generator Loss: 0.1050
Epoch 1/1 Step 5200... Discriminator Loss: 2.2208... Generator Loss: 0.1871
Epoch 1/1 Step 5300... Discriminator Loss: 2.5029... Generator Loss: 0.1675
Epoch 1/1 Step 5400... Discriminator Loss: 3.1147... Generator Loss: 0.0761
Epoch 1/1 Step 5500... Discriminator Loss: 2.2961... Generator Loss: 0.1722
Epoch 1/1 Step 5600... Discriminator Loss: 2.9771... Generator Loss: 0.1343
Epoch 1/1 Step 5700... Discriminator Loss: 2.5695... Generator Loss: 0.1322
Epoch 1/1 Step 5800... Discriminator Loss: 2.6478... Generator Loss: 0.1232
Epoch 1/1 Step 5900... Discriminator Loss: 2.7235... Generator Loss: 0.1036
Epoch 1/1 Step 6000... Discriminator Loss: 3.0648... Generator Loss: 0.0761
Epoch 1/1 Step 6100... Discriminator Loss: 2.4741... Generator Loss: 0.1345
Epoch 1/1 Step 6200... Discriminator Loss: 2.9228... Generator Loss: 0.0918
Epoch 1/1 Step 6300... Discriminator Loss: 2.8520... Generator Loss: 0.0950

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.